We are addressing the key questions of:
| Median | Upper 50 CI | Lower 50 CI | |
|---|---|---|---|
| Peak Hospitalizations | 3,917.00 | 7,233.00 | 2,098.00 |
| Deaths by August 1, 2020 | 14,791.00 | 25,695.75 | 10,490.25 |
| Detected Illnesses by August 1,2020 | 483,235.50 | 713,865.50 | 312,448.75 |
| Total Illnesses by August 1, 2020 | 4,438,658.50 | 6,043,550.50 | 2,226,784.75 |
| Proportion of Cases Detected (%) | 12.81 | 19.26 | 9.49 |
| CFR Based on Observed Illnesses (%) | 3.56 | 4.46 | 2.94 |
| CFR Based on Total Illnesses (%) | 0.45 | 0.72 | 0.30 |
| R0 - before social distancing | 3.02 | 3.45 | 2.59 |
| % Reduction in Social Contacts (March 15 - ) | 55.45 | 50.65 | 60.57 |
Dashed line = Maximum possible capacity (i.e., total licensed hospital beds, ICU beds, ventilators) in L.A. County
Demonstrating model fit against COVID-19 data for Los Angeles, for the following variables:
COVID-19 data is shown as black dots in the figures below.
We analyze how population prevalence of known COVID-19 risk factors: advanced age, existence of other health conditions or comorbidities, smoking status, and obesity status, affect COVID-19 illness trajectories in L.A. County and spatial subdivisions.
First, we estimate the conditional probability of COVID illness severity given combinations of risk factors. We categorize the population into a number of risk profiles, representing different combinations of known COVID-19 risk factors: advanced age, existence of other health conditions or comorbidities, smoking status, and obesity status. Using previous COVID-19 studies reporting the marginal risk of severe COVID-19 outcomes given individual risk factors, we develop a statistical model to estimate the probability of COVID illness trajectories for individuals having or not having combinations of risk factors represented by these risk profiles. Specifically, we estimate the probability that individuals within a specific risk group are admitted to hospital given having acquired illness \(Pr(Hospital | Illness, Profile_i)\), are admitted to the ICU given admittance to hospitalized \(Pr(ICU | Hospital, Profile_i)\), and that die given being admitted to the ICU \(Pr(Death | ICU, Profile_i)\). More information is provided below under Methods and Data.
For the analysis below, we have grouped the multiple risk profiles into 5 key risk groups according to similar within-group levels of the probabilities \(Pr(Hospital | Illness, Profile_i)\), \(Pr(ICU | Hospital, Profile_i)\), and \(Pr(Death | ICU, Profile_i)\).
Second, we use these probabilities to estimate the proportion of each risk group that will make up the resulting cohorts of COVID patients admitted to hospital, admitted to ICU, or that die within the L.A. County population, based on the prevalence of each risk group in the population.
Results are also presented for each Service Planning Area (SPA) population within L.A. County. A SPA is a specific geographical region within Los Angeles County used by the Department of Public Health to plan and provide health services. There are 8 SPAs in Los Angeles County.